During FY16, BIVS continued to support the small Telenephrology System, which it had constructed to provide Andrew Narva, MD, Director, National Kidney Disease Education Program, NIDDK, with the ability to conduct regularly scheduled clinics with his renal patients at the Zuni PHS Hospital in Zuni, AZ. In April 2016, the broadband communication link from the main NIH campus to Zuni, AZ was decommissioned, and the NIH terminus of the Telenephrology Clinic was relocated to Dr. Narvas new off-campus office. Technical support for the systems new components and communication link was transferred from BIVS to NIDDKs IT staff. BIVS support of this activity has ended. An MRI research project, initiated in FY13, involved experimental and clinical studies directed toward the possibility of the use of MRI methodology for the early detection of prostate cancer and the development of a measure of aggressiveness. This collaboration with Peter L. Choyke, MD, Senior Investigator and Director, Medical Imaging Program, Center for Cancer Research, NCI, along with the BIVS staff member participating, was relocated to another section within CIT/OIR at the end of FY15. BIVS support of this activity has ended. In FY16, development has slowly continued on a novel research-oriented Stereo Medical Image Display System, which is being developed in the JAVA Language, and will be compatible with the Medical Image Processing Analysis and Visualization (MIPAV) Application designed by the Biomedical Imaging Research Services Section (BIRSS), OIR, CIT. The implementation of software stereo image generation will utilize a group of algorithms developed at Johns Hopkins University, which are organized as plug-ins to the MIPAV environment. This Stereo Medical Image Display System will be controlled by hand-motion, and it is being developed to support brain imaging as the initial target application. Powered by a high-performance workstation containing dual quad-core processors, and a sophisticated Quadro 5000 Graphics Card. This development platform will ultimately be controlled by a 3D position-sensing haptics glove with internal tactile feedback, and will have speech recognition capability. During FY10, BIVS was asked by William A. Gahl, MD, PhD, Chief of the NHGRI Undiagnosed Diseases Program (UDP), and Murat Sincan, MD, Research Fellow, NHGRI, to create a secure UDP Case Management Portal, which contains a 12-Terabyte Disc Array, as well as the Galaxy Web-based Platform that was developed by Penn State University and supports data-intensive biomedical research. The Portal was designed to provide outside subject matter experts a method for secure login to the NIH, in order to assist in the diagnosis of undiagnosed diseases. During FY16, the UDP Case Management Portal was decommissioned, due to staff departures and the elimination of the international outreach effort using this methodology. BIVS support of this activity has ended. During FY16, BIVS continued work on a collaborative project with Henry Masur, MD, Chief, Critical Care Medicine Department (CCMD), CC, and Naomi P. O'Grady, MD, Staff Clinician, CCMD, CC, aimed at the development of novel methods for the graphical presentation of the status of patients within a critical care environment. The prototype system, departs from the electronic spreadsheet display format that had long been the gold standard for patient status display in a modern critical care unit, and is being implemented on the iPad platform to capitalize upon multi-touch display technology and swipe screen control capability. Software development for the mobile Intensive Care Unit (mICU) Clinical Information System (CIS) Project began in January 2012. The associated Data Gateway is expected to eventually allow patient data from the Clinical Center (CC) Medical Information System (MIS) to be channeled to the iPad, via the buildings wireless network, in an encrypted format. Currently, manually generated data for three simulated patients is transmitted from the Data Gateway, via double encryption, to the iPads in the hands of CCMD staff. NIH single-factor authentication is integrated within the mICU CIS Application, in order to provide a pathway towards two-factor authentication. Specialized cases, with integrated PIV card readers, are attached to the iPads utilized in this project. For added security, these iPads will ultimately require two-factor authentication during login to the mICU CIS App. The mICU CIS Application currently provides a demonstration of the Clinical Data Viewer (CDV) Function, as seen on the CC MIS, and the Clinical Graphics Viewer (CGV) Function that provides novel Circle Diagram displays of physiologic parameters, respiratory parameters, clinical lab values, etc. In addition, an Electrocardiogram Waveform Display and Playback (ECG) Function is provided. The mICU CIS Application provides a direct connection to the NIH Library's Journal Download website and NLM's PubMed website; Bookcase Function for downloaded .pdf files; Medical Camera Function; Medical Photo Album Function; Direct access to the UpToDate(R) website; and Direct Access to the Micromedex (R) website. The development of ECG arrhythmia detection and display algorithms was initiated, with the ultimate goal of including these capabilities within the mICU CIS Application in the future. During FY16, the implementation of a novel display methodology has been finalized for pharmaceuticals given via IV infusion routes, and sample data has been generated for inclusion in the simulated patients data sets. Licensing issues related to the PIV card middleware are still being resolved. At the beginning of FY16, A new project was initiated with Kevin L. Briggman, Ph.D., Director, Circuit Dynamics and Connectivity Unit (CDCU), NINDS. This activity involves the development of an Image Stack Visualization Engine (ISVE) for an iPad environment, which allows 3D visualization and rotation of a stack of Serial Block-face scanning Electron Microscopy (SBEM) images of brain tissue from the zebrafish and mouse. The 3D ISVE will be the main component of an iPad App being developed, which will allow the elucidation of the connectome of the zebrafish or mouse brain though the use of crowdsourcing methodology. Crowdsourcing will greatly accelerate the process of adjudicating conflicts in the first guess at the connectome, which was independently produced by NINDS staff utilizing a neural network to preprocess the SBEM imagery. The iPad App will overlay the original SBEM images with the preprocessed connectome estimates, for review and editing by volunteers from the crowdsourcing community.